def compute_PSF(filename):
"""
Modelize the PSF using GROOT model for aniso and bandwidth, Gendron model for aliasing,
dphi_highpass for fitting, noise extracted from datas. Non linearity not taken into account
:parameters:
filename: (str) : ROKET hdf5 file path
:return:
psf: (np.ndarray) : PSF
"""
tic = time.time()
spup = drax.get_pup(filename)
Cab = compute_Cerr(filename)
Cn = compute_Cn_cpu(filename)
Ca = compute_Calias(filename)
Cee = Cab + Cn + Ca
otftel, otf2, psf, gpu = gamora.psf_rec_Vii(filename,
fitting=False,
cov=(Cee).astype(np.float32))
otf_fit, psf_fit = compute_OTF_fitting(filename, otftel)
psf = np.fft.fftshift(np.real(np.fft.ifft2(otf_fit * otf2 * otftel)))
psf *= (psf.shape[0] * psf.shape[0] / float(np.where(spup)[0].shape[0]))
tac = time.time()
print("PSF computed in ", tac - tic, " seconds")
return psf
def add_fitting_to_psf(filename, otf, otffit):
"""
Compute the PSF including the fitting OTF
:parameters:
otf: (np.ndarray[ndim=2, dtype=np.float32]): OTF
otffit: (np.ndarray[ndim=2, dtype=np.float32]): Fitting error OTF
:return:
psf: (np.ndarray[ndim=2, dtype=np.float32]): PSF
"""
print("\nAdding fitting to PSF...")
spup = drax.get_pup(filename)
psf = np.fft.fftshift(np.real(np.fft.ifft2(otffit * otf)))
psf *= (psf.shape[0] * psf.shape[0] / float(np.where(spup)[0].shape[0]))
return psf
def compute_OTF_fitting(filename, otftel):
"""
Modelize the OTF due to the fitting using dphi_highpass
:parameters:
filename: (str) : ROKET hdf5 file path
otftel: (np.ndarray) : Telescope OTF
:return:
otf_fit: (np.ndarray) : Fitting OTF
psf_fit (np.ndarray) : Fitting PSF
"""
f = h5py.File(filename, 'r')
r0 = f.attrs["_Param_atmos__r0"] * (f.attrs["_Param_target__Lambda"][0] /
0.5)**(6. / 5.)
ratio_lambda = 2 * np.pi / f.attrs["_Param_target__Lambda"][0]
# Telescope OTF
spup = drax.get_pup(filename)
mradix = 2
fft_size = mradix**int((np.log(2 * spup.shape[0]) / np.log(mradix)) + 1)
mask = np.ones((fft_size, fft_size))
mask[np.where(otftel < 1e-5)] = 0
x = np.arange(fft_size) - fft_size / 2
pixsize = f.attrs["_Param_tel__diam"] / f.attrs["_Param_geom__pupdiam"]
x = x * pixsize
r = np.sqrt(x[:, None] * x[:, None] + x[None, :] * x[None, :])
tabx, taby = starlord.tabulateIj0()
dphi = np.fft.fftshift(
starlord.dphi_highpass(
r, f.attrs["_Param_tel__diam"] /
(f.attrs["_Param_dm__nact"][0] - 1), tabx, taby) *
(1 / r0)**(5 / 3.)) # * den * ratio_lambda**2 * mask
otf_fit = np.exp(-0.5 * dphi) * mask
otf_fit = otf_fit / otf_fit.max()
psf_fit = np.fft.fftshift(np.real(np.fft.ifft2(otftel * otf_fit)))
psf_fit *= (fft_size * fft_size / float(np.where(spup)[0].shape[0]))
f.close()
return otf_fit, psf_fit
def psf_rec_vii_cpu(filename):
"""
PSF reconstruction using Vii functions (CPU version)
:parameters:
filename: (str): path to the ROKET file
:return:
otftel: (np.ndarray[ndim=2, dtype=np.float32]): OTF of the perfect telescope
otf2: (np.ndarray[ndim=2, dtype=np.float32]): OTF due to residual phase error
psf: (np.ndarray[ndim=2, dtype=np.float32]): LE PSF
"""
f = h5py.File(filename, 'r')
IF, T = drax.get_IF(filename)
ratio_lambda = 2 * np.pi / f.attrs["_Param_target__Lambda"][0]
# Telescope OTF
print("Computing telescope OTF...")
spup = drax.get_pup(filename)
mradix = 2
fft_size = mradix**int((np.log(2 * spup.shape[0]) / np.log(mradix)) + 1)
pup = np.zeros((fft_size, fft_size))
pup[:spup.shape[0], :spup.shape[0]] = spup
pupfft = np.fft.fft2(pup)
conjpupfft = np.conjugate(pupfft)
otftel = np.real(np.fft.ifft2(pupfft * conjpupfft))
den = 1. / otftel
den[np.where(np.isinf(den))] = 0
mask = np.ones((fft_size, fft_size))
mask[np.where(otftel < 1e-5)] = 0
otftel = otftel / otftel.max()
print("Done")
# Covariance matrix
print("Computing covariance matrix...")
err = drax.get_err(filename)
P = f["P"][:]
err = P.dot(err)
Btt = f["Btt"][:]
#modes = IF.T.dot(Btt)
covmodes = err.dot(err.T) / err.shape[1]
print("Done")
# Vii algorithm
print("Diagonalizing cov matrix...")
e, V = np.linalg.eig(covmodes)
print("Done")
tmp = np.zeros((fft_size, fft_size))
newmodek = tmp.copy()
ind = np.where(pup)
for k in range(err.shape[0]):
#newmodek[ind] = IF.T.dot(V[:,k])
#newmodek[ind] = modes.dot(V[:,k])
tmp2 = Btt.dot(V[:, k])
newmodek[ind] = IF.T.dot(tmp2[:-2])
newmodek[ind] += T.T.dot(tmp2[-2:])
term1 = np.real(np.fft.fft2(newmodek**2) * conjpupfft)
term2 = np.abs(np.fft.fft2(newmodek))**2
tmp += ((term1 - term2) * e[k])
print(" Computing Vii : %d/%d\r" % (k, covmodes.shape[0]), end=' ')
print("Vii computed")
dphi = np.real(np.fft.ifft2(2 * tmp)) * den * mask * ratio_lambda**2
otf2 = np.exp(-0.5 * dphi) * mask
otf2 = otf2 / otf2.max()
psf = np.fft.fftshift(np.real(np.fft.ifft2(otftel * otf2)))
psf *= (fft_size * fft_size / float(np.where(pup)[0].shape[0]))
f.close()
return otftel, otf2, psf
def psf_rec_Vii(filename, err=None, fitting=True, covmodes=None, cov=None):
"""
PSF reconstruction using Vii functions with GPU acceleration.
:parameters:
filename: (str): path to the ROKET file
err: (np.ndarray[ndim=2, dtype=np.float32]): (optionnal) Buffers of command error
fitting: (bool): (optional) Add the fitting error to the PSF or not (True by default)
covmodes: (np.ndarray[ndim=2, dtype=np.float32]): (optionnal) Error covariance matrix in the modal space
cov: (np.ndarray[ndim=2, dtype=np.float32]): (optionnal) Error covariance matrix in the DM space
:return:
otftel: (np.ndarray[ndim=2, dtype=np.float32]): OTF of the perfect telescope
otf2: (np.ndarray[ndim=2, dtype=np.float32]): OTF due to residual phase error
psf: (np.ndarray[ndim=2, dtype=np.float32]): LE PSF
gpu: (Gamora): Gamora GPU object (for manipulation and debug)
"""
f = h5py.File(filename, 'r')
spup = drax.get_pup(filename)
# Sparse IF matrix
IF, T = drax.get_IF(filename)
# Covariance matrix
P = f["P"][:]
print("Projecting error buffer into modal space...")
if ((err is None) and (cov is None)):
err = drax.get_err(filename)
err = P.dot(err)
print("Computing covariance matrix...")
if (cov is None):
if (covmodes is None):
covmodes = err.dot(err.T) / err.shape[1]
else:
covmodes = (P.dot(covmodes)).dot(P.T)
else:
covmodes = cov
print("Done")
Btt = f["Btt"][:]
# Scale factor
scale = float(2 * np.pi / f.attrs["_Param_target__Lambda"][0])
# Init GPU
gpu = Gamora(c, c.activeDevice, "Vii", Btt.shape[0], covmodes.shape[0],
f["noise"][:].shape[1], IF.data, IF.indices, IF.indptr, IF.data.size, T,
spup, spup.shape[0],
np.where(spup)[0].size, scale, Btt, covmodes)
# Launch computation
# gamora.set_eigenvals(e)
# gamora.set_covmodes(V)
tic = time.time()
gpu.psf_rec_Vii()
otftel = np.array(gpu.d_otftel)
otf2 = np.array(gpu.d_otfVii)
otftel /= otftel.max()
if (list(f.keys()).count("psfortho") and fitting):
print("\nAdding fitting to PSF...")
psfortho = f["psfortho"][:]
otffit = np.real(np.fft.fft2(psfortho))
otffit /= otffit.max()
psf = np.fft.fftshift(np.real(np.fft.ifft2(otffit * otf2)))
else:
psf = np.fft.fftshift(np.real(np.fft.ifft2(otftel * otf2)))
psf *= (psf.shape[0] * psf.shape[0] / float(np.where(spup)[0].shape[0]))
f.close()
tac = time.time()
print(" ")
print("PSF renconstruction took ", tac - tic, " seconds")
return otftel, otf2, psf, gpu